Implementation of an English Machine Translation System based on Deep Learning

English machine translation system is designed based on deep learning. The system architecture includes data preprocessing, encoder, decoder, optimizer and loss function. In the data preprocessing stage, the validity of the training data is ensured by cleaning and labeling the original data. The encoder uses a self-focused mechanism to efficiently capture the context information in the input sequence, while the decoder uses the Adam optimizer and the cross-entropy loss function to generate the translation results of the target language to improve the translation accuracy. To verify the effectiveness of the system, the experimental results show that the designed deep learning system is better than the traditional translation methods when processing complex sentence patterns and long sentences.